@Dave Apache Samoa seemed like a good starting point as they’ve already
implemented a set of algorithms as storm topologies, additionally regardless of
whether that community is active , we could still take that code and iterate on
it within heron as a good starting point. To that end I feel
Hmm. Good question. Maybe not yet reaching out.
On Thu, Jul 5, 2018 at 11:49 AM, Dave Fisher wrote:
> Hi -
>
> Has anyone reached out to the SAMOA podling? Or is their architecture
> inverted from that being proposed I’m not sure how well the SAMOA community
> is doing as they have had low
Hi -
Has anyone reached out to the SAMOA podling? Or is their architecture inverted
from that being proposed I’m not sure how well the SAMOA community is doing as
they have had low activity since early this year.
Regards,
Dave
> On Jun 29, 2018, at 11:01 PM, Ning Wang wrote:
>
> Brief notes
Brief notes for the meeting on June 29:
- We need to hook up heron with Apache samoa. Saikat to create new issues
in github.
- Create a slack channel: #machine-learning
- Let's add potential use cases in the design doc:
Brief notes for the meeting on June 22th:
- still studying the documents.
--- https://mapr.com/blog/monitoring-real-time-uber-
data-using-spark-machine-learning-streaming-and-kafka-api-part-2/
--- https://databricks.com/blog/2018/06/05/introducing-mlflow-
Hi Dave,
The Samoa piece is a bit tricky, the goal essentially is to take their storm
components and enhance them to work within the heron storm subcomponent and
eventually with heron streamlet architecture. We chose Samoa because they have
already built several machine learning topologies
Sent from my iPhone
> On Jun 8, 2018, at 5:08 PM, Ning Wang wrote:
>
> Brief notes for today's meeting:
>
> - Review DD:
> https://docs.google.com/document/d/1LrO7XRcMxJoMM83wjRd-Ov74VAaomA_mXOAhCStgGng/edit
The document says copying Samoa. Heron should be working with the Samoa team
and
Brief notes for today's meeting:
- Review DD:
https://docs.google.com/document/d/1LrO7XRcMxJoMM83wjRd-Ov74VAaomA_mXOAhCStgGng/edit
- We want to understand better about the bigger picture of ML in stream
processing systems.
-- talk to ML users
-- doc of related systems to read:
---
